| Challenge: | a recent study shows that novices perform better than experts in a code quiz. |
| Approach: | They present a minimalist neural machine translation toolkit based on PyTorch . they evaluate the accessibility of the toolkit in a user study . |
| Outcome: | The proposed toolkit performs comparable to more complex toolkits on standard benchmarks. |
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| Challenge: | JoeyS2T is a simple, lightweight NMT extension for speech-to-text tasks such as automatic speech recognition and end-to end speech translation. |
| Approach: | JoeyS2T is a JoeyNMT extension for automatic speech recognition and end-to-end speech translation. |
| Outcome: | JoeyS2T performs competitively on English speech recognition and English-to-German speech translation benchmarks. |
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation tasks. |
YANMTT: Yet Another Neural Machine Translation Toolkit (2023.acl-demo)
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| Challenge: | Neural machine translation (NMT) is an end-to-end approach that provides stateof-the-art results for a variety of language pairs. |
| Approach: | They propose to build an open-source neural machine translation toolkit on top of HuggingFace's Transformers library and use it for pre-training and fine-tuning sequence-to-sequence models. |
| Outcome: | The proposed toolkit is built on top of the HuggingFace Transformers library and provides advanced features such as document/multi-source NMT, simultaneous NMT and mixtures-of-experts. |
A Hyperparameter Optimization Toolkit for Neural Machine Translation Research (2023.acl-demo)
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| Challenge: | Insufficient tuning of hyperparameters may lead to poor results and exaggerated results . lack of open-source support tools means that the level of rigor in hyperparametric optimization may vary widely. |
| Approach: | They propose a hyperparameter optimization toolkit for neural machine translation that is implemented as a wrapper on top of the open-source Sockeye NMT software. |
| Outcome: | The proposed toolkit is implemented as a wrapper on top of the open-source Sockeye NMT software. |
jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models (2020.acl-demos)
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Yada Pruksachatkun, Phil Yeres, Haokun Liu, Jason Phang, Phu Mon Htut, Alex Wang, Ian Tenney, Samuel R. Bowman
| Challenge: | jiant is an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. |
| Approach: | They introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks. |
| Outcome: | The proposed toolkit reproduces published performance on GLUE and SuperGLUE tasks. |
Pre-training Methods for Neural Machine Translation (2021.acl-tutorials)
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| Challenge: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Approach: | This tutorial provides a comprehensive guide to make the most of pre-training for neural machine translation. |
| Outcome: | This tutorial explains how to make the most of pre-training for neural machine translation. |
Competence-based Curriculum Learning for Neural Machine Translation (N19-1)
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| Challenge: | Existing NMT systems require specialized heuristics and large batch sizes. |
| Approach: | They propose a curriculum learning framework for NMT that reduces training time and costs . framework consists of a principled way of deciding which training samples are shown to the model . |
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Unsupervised Neural Machine Translation with Weight Sharing (P18-1)
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| Challenge: | Unsupervised neural machine translation (NMT) is a new approach for machine translation . the model uses only one shared encoder to map pairs of sentences from different languages to a shared-latent space . |
| Approach: | They propose an unsupervised approach which trains the model without labeling data . they propose two independent encoders but share some partial weights to extract high-level representations of input sentences. |
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Digging Errors in NMT: Evaluating and Understanding Model Errors from Partial Hypothesis Space (2022.emnlp-main)
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| Challenge: | Current evaluation of neural machine translation systems is limited by one best hypothesis and search errors brought by heuristic decoding algorithms. |
| Approach: | They propose a new evaluation protocol which defines model errors with model’s ranking capability over hypothesis space and Monte Carlo sampling evaluation to tackle the problem of exponentially large space. |
| Outcome: | The proposed evaluation protocol is consistent with what is currently used in the field and is consistent to what is being proposed. |
A Survey of Domain Adaptation for Neural Machine Translation (C18-1)
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| Challenge: | Neural machine translation (NMT) is a deep learning based approach for machine translation. |
| Approach: | They propose to use a deep learning approach to train machine translation in scenarios where large-scale parallel corpora are available. |
| Outcome: | The proposed approach yields the state-of-the-art translation performance in resource rich scenarios. |